Books

Risk management has become one of the key requirements for insightful decision-making. What are risks sources? How are they being managed? This book describes certainty, uncertainty, financial risks, methods of risk mitigation, and risk management. The first chapter of this book represents some milestones in risk management and introduces the main aspects of financial risk management. The following chapters discuss various types of financial risk such as market risk, credit risk, operational risk, liquidity risk, interest rate risk, and other financial risks. The last chapter describes enterprise risk management which binds together all the risks. This book, which is accompanied by PowerPoint presentations, is aimed at lecturers, students, and practitioners with an interest in risk management. The book is the fruit of the authors' long years of work in the field of risk management, serving as a risk management advisor and teaching an MBA-level academic course on the topic for economics and business administration students.

This book is a self-contained introduction to engineering and testing machine learning (ML) systems. It systematically discusses and teaches the art of crafting and developing software systems that include and surround machine learning models. Crafting ML based systems that are business-grade is highly challenging, as it requires statistical control throughout the complete system development life cycle. To this end, the book introduces an “experiment first” approach, stressing the need to define statistical experiments from the beginning of the development life cycle and presenting methods for careful quantification of business requirements and identification of key factors that impact business requirements. Applying these methods reduces the risk of failure of an ML development project and of the resultant, deployed ML system. The presentation is complemented by numerous best practices, case studies and practical as well as theoretical exercises and their solutions, designed to facilitate understanding of the ideas, concepts and methods introduced.

The goal of this book is to empower scientists, engineers, and software developers with the knowledge and skills necessary to create robust and reliable ML software.

The Encyclopedia of Knowledge Management, now in its second edition, provides a compendium of terms, definitions and explanation

Knowledge management in general, and Internet-based knowledge management in particular, is one of the foremost strategic directions being investigated and adopted by corporations today. The promises of better decision making, faster turnaround times, improved organizational communication, and higher levels of cooperation and interaction among personnel, have all combined to create a holy grail kind of aura. Yet, like the grail the goals here are elusive, and the road to reaching them is long and fraught with pitfalls. Each of us, as individuals, performs a variety of functions that can be termed knowledge management. We remember things: names, numbers, experiences, and procedures. We know how to do things such as ride a bike; bake a cake; calculate a derivative; fix a flat tire. We know where to find information that we don’t remember on our own: we write things down; file them; enter them in a PDA. Some of us do it better, some of us are chronically disorganized, but at the end of the day each of us is performing his or her own knowledge management function.

Cooperating Heterogeneous Systems provides an in-depth introduction to the issues and techniques surrounding the integration and control of diverse and independent software components. Organizations increasingly rely upon diverse computer systems to perform a variety of knowledge-based tasks. This presents technical issues of interoperability and integration, as well as philosophical issues of how cooperation and interaction between computational entities is to be realized. Cooperating systems are systems that work together towards a common end. The concepts of cooperation must be realized in technically sound system architectures, having a uniform meta-layer between knowledge sources and the rest of the system. The layer consists of a family of interpreters, one for each knowledge source, and meta-knowledge. A system architecture to integrate and control diverse knowledge sources is presented. The architecture is based on the meta-level properties of the logic programming language Prolog. An implementation of the architecture is described, a Framework for Logic Programming Systems with Distributed Execution (FLiPSiDE). Knowledge-based systems play an important role in any up-to-date arsenal of decision support tools. The tremendous growth of computer communications infrastructure has made distributed computing a viable option, and often a necessity in geographically distributed organizations. It has become clear that to take knowledge-based systems to their next useful level, it is necessary to get independent knowledge-based systems to work together, much as we put together ad hoc work groups in our organizations to tackle complex problems. The book is for scientists and software engineers who have experience in knowledge-based systems and/or logic programming and seek a hands-on introduction to cooperating systems. Researchers investigating autonomous agents, distributed computation, and cooperating systems will find fresh ideas and new perspectives on well-established approaches to control, organization, and cooperation.